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Appl. Sci. 2018, 8(8), 1349; https://doi.org/10.3390/app8081349

Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking

Department of Image, Chung-Ang University, Seoul 06974, Korea
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Received: 30 May 2018 / Revised: 6 August 2018 / Accepted: 7 August 2018 / Published: 10 August 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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Abstract

In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers. View Full-Text
Keywords: computer vision; object tacking; correlation filter; partial block; scale space; adaptive learning; discriminative model; partial occlusion; scale variation computer vision; object tacking; correlation filter; partial block; scale space; adaptive learning; discriminative model; partial occlusion; scale variation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Jeong, S.; Paik, J. Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking. Appl. Sci. 2018, 8, 1349.

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